Stars lie. Behavioral signals don't.
An MCP server and web tool that scores npm packages, PyPI packages, and GitHub repos on behavioral commitment — signals that are harder to fake than stars, READMEs, or download counts.
Three packages in a typical Node.js project are CRITICAL right now:
- chalk — 399M downloads/week, 1 maintainer
- zod — 139M downloads/week, 1 maintainer
- axios — 96M downloads/week, 1 maintainer (attacked April 1st, 2026)
Stars and README quality don't surface this. Behavioral signals do.
Terminal (zero install):
npx proof-of-commitment axios zod chalk
# or scan your own project:
npx proof-of-commitment --file package.json
# NEW: scan ALL transitive dependencies via lock file:
npx proof-of-commitment --file package-lock.json # npm
npx proof-of-commitment --file yarn.lock # yarn
npx proof-of-commitment --file pnpm-lock.yaml # pnpm
# PyPI too:
npx proof-of-commitment --pypi litellm langchain requestsWeb demo (no install): getcommit.dev/audit — paste your packages, see risk scores in seconds.
MCP server (zero install):
{
"mcpServers": {
"proof-of-commitment": {
"type": "streamable-http",
"url": "https://poc-backend.amdal-dev.workers.dev/mcp"
}
}
}Add to Claude Desktop, Cursor, Windsurf, or any MCP-compatible AI tool. Then ask:
"Audit my package.json for supply chain risk" "Score axios, zod, chalk, lodash — which is highest risk?" "Is vercel/ai actively maintained?"
Add supply chain auditing to any CI pipeline — auto-detects packages from package.json or requirements.txt, posts results as a PR comment, writes to GitHub Step Summary, and optionally fails on CRITICAL packages.
# .github/workflows/supply-chain-audit.yml
name: Supply Chain Audit
on: [push, pull_request]
jobs:
audit:
runs-on: ubuntu-latest
permissions:
pull-requests: write # needed for PR comments
steps:
- uses: actions/checkout@v4
- uses: piiiico/proof-of-commitment@main
with:
fail-on-critical: false # set true to block merges
comment-on-pr: true # posts audit table directly on the PRWhen comment-on-pr: true (default), the action automatically posts the audit table as a comment on the pull request — and updates the same comment on re-run, so you don't get comment spam. Reviewers see the risk table without leaving the PR.
Inputs:
| Input | Default | Description |
|---|---|---|
packages |
(auto) | Comma-separated package names (auto-detected from package.json/requirements.txt if not set) |
fail-on-critical |
true |
Fail the workflow if CRITICAL packages are found |
max-packages |
20 |
Max packages to audit when auto-detecting |
comment-on-pr |
true |
Post audit results as a PR comment (requires pull-requests: write permission) |
Outputs: has-critical, critical-count, audit-summary (markdown table, also written to Step Summary).
Example PR comment / Step Summary output:
| Package | Risk | Score | Maintainers | Downloads/wk | Age |
|---------|-------------|-------|-------------|--------------|-------|
| chalk | 🔴 CRITICAL | 75 | 1 | 380M | 12.7y |
| zod | 🔴 CRITICAL | 83 | 1 | 133M | 6.1y |
| axios | 🔴 CRITICAL | 89 | 1 | 93M | 11.6y |
Add a commitment score badge to any package you maintain or depend on:
Examples:
| Package | Badge URL |
|---|---|
| axios |  |
| zod |  |
| litellm |  |
Colors: 🟢 healthy (75+) · 🟡 good (60–74) · 🟡 moderate (40–59) · 🟠 high risk (<40) · 🔴 CRITICAL (single maintainer + >10M downloads/week)
Badges are cached 5 minutes at Cloudflare's edge. No API key needed.
No API key. No install.
curl https://poc-backend.amdal-dev.workers.dev/api/audit \
-X POST \
-H "Content-Type: application/json" \
-d '{"packages": ["axios", "zod", "chalk", "lodash", "express"]}'{
"count": 5,
"results": [
{
"name": "chalk",
"ecosystem": "npm",
"score": 75,
"maintainers": 1,
"weeklyDownloads": 398397580,
"ageYears": 12.7,
"trend": "stable",
"riskFlags": ["CRITICAL"]
},
...
]
}| Tool | Description |
|---|---|
audit_dependencies |
Batch risk audit for up to 20 npm/PyPI packages |
lookup_npm_package |
Single npm package behavioral profile |
lookup_pypi_package |
Single PyPI package behavioral profile |
lookup_github_repo |
GitHub repo commitment score (longevity, commit frequency, contributor depth) |
lookup_business |
Norwegian business register — operating years, employees, financials |
lookup_business_by_org |
Same, by org number |
query_commitment |
Browser extension behavioral data (unique verified visitors, repeat rate) |
Each package is scored 0–100 across:
- Longevity — How long has the package existed? Abandoned packages get reactivated for attacks.
- Maintainer depth — Single maintainer + millions of weekly downloads = the attack surface LiteLLM exploited.
- Release consistency — Regular releases signal active oversight. Long gaps = vulnerability accumulation.
- Download trend — Growing packages attract more scrutiny (and attacks). Stable = lower profile.
Risk flags:
CRITICAL— single maintainer + >10M weekly downloads (exact LiteLLM/axios attack profile)HIGH— package <1yr old + rapid adoptionWARN— no release in 12+ months
chalk — score 75, 1 maintainer, 399M/week ⚑ CRITICAL
zod — score 83, 1 maintainer, 139M/week ⚑ CRITICAL
axios — score 89, 1 maintainer, 96M/week ⚑ CRITICAL (attacked Apr 1 2026)
lodash — score 88, 3 maintainers, 68M/week
express — score 91, 5 maintainers, 35M/week
litellm — score 74, 1 maintainer ⚑ CRITICAL (supply chain attack Mar 2026)
The LiteLLM attack (March 2026) and axios attack (April 2026) followed the same pattern: stolen credentials → malicious package pushed → 97M+ machines exposed. Both packages scored CRITICAL by these metrics before the attacks.
Declarative signals (stars, README quality, CI badges) don't capture this risk. Behavioral commitment does.
registry.modelcontextprotocol.io → io.github.piiiico/proof-of-commitment
| Layer | Technology |
|---|---|
| Backend | Cloudflare Workers + D1 |
| MCP | Model Context Protocol SDK |
| Data | npm registry, PyPI, GitHub API, Brønnøysund (NO) |
| Landing | Astro + Cloudflare Pages |
Planned, not promised. The project is early-stage — contributions welcome on any of these.
| Feature | Status | Notes |
|---|---|---|
| Cargo (Rust) registry support | Planned | Extend the npm/pypi scoring pattern to crates.io |
| Go modules support | Planned | pkg.go.dev API + GitHub backing score |
| Score breakdown visualization | Planned | Chart component for the 5 dimensions on getcommit.dev/audit |
--json flag for CLI |
Planned | Structured output for CI/CD pipelines (jq, --fail-on-critical) |
| pnpm workspace monorepo support | Planned | Detect pnpm-workspace.yaml, audit all packages |
| Historical score tracking | Planned | Trend charts — was this package getting riskier over time? |
| Org-level dashboards | Planned | Aggregate risk view across all repos in a GitHub org |
See open issues for things you can help with today.
Supply chain auditing is the first tool. The underlying primitive is a commitment graph — behavioral signals that replace content-based trust across any domain.
When content is free to fake (reviews, stars, READMEs), commitment becomes the signal. A maintainer who has shipped 847 releases over 12 years is a different kind of commitment than one who published once in 2023.
The same logic applies to websites, businesses, and AI agents. Two card networks have independently named this gap: Mastercard Verifiable Intent §9.2 explicitly lists behavioral trust as "not covered." Visa TAP identifies agents without answering whether to trust them.
Proof of Commitment is the trust layer they're pointing at.
bun install
bun run dev:backend # local server with SQLite
bun run test:e2e # E2E test with mock World IDDeploy:
bun run deploy # deploys to Cloudflare Workers